驾驶疲劳恢复时间的心电信号分析

适当的休息能有效缓解驾驶疲劳,提高行车安全。为探究科学的驾驶休息时间,开展试验,用MP150多通道生理信号采集仪,采集20名驾驶人4 h模拟驾驶作业后休息时的心电(ECG)信号,分析ECG信号的变化规律。结果表明:在驾驶疲劳恢复期,ECG指标心率(HR)、间期标准差(SDNN)、低高频比值(LF/HF)、样本熵(SampEn)与恢复时间高度线性相关,且性别差异对疲劳恢复期ECG指标有显著影响;驾驶人的心理疲劳恢复时间早于生理疲劳恢复时间;驾驶人主观恢复时间相对于客观疲劳恢复时间有提前现象;建议连续驾驶4 h后男性驾驶人休息时间不少于24 min,女性驾驶人休息时间不少于27 min。     

基于生理信号的气味对运动性疲劳的影响分析

利用MP150多道生理仪记录心电和脑电等生理信号的变化,从嗅觉的角度,研究2种嗅觉刺激气体(桉树精油和工业废水)对运动性疲劳的影响,并以静息组做对照进行了3组试验。结果表明:1)方差分析显示,关于HR、MRR、Uα等静息组和精油组p0.05,达到显著性水平;2)心电数据分析显示,3组试验心率值均处于下降趋势,精油组下降速度最快,且下降的幅度最大;3)脑电数据分析显示,脑能量参数(Uα+Uθ)/Uβ的变化说明疲劳程度先增加后降低,嗅吸过精油后脑能量上升的幅度最小,开始下降的时间最早,且下降的幅度相对较大。这表明精油和工业废水对人体产生了不同的影响,但精油对心电和脑电信号影响较大,对运动后心率和疲劳的恢复有较好的作用。桉树精油能更好地缓解运动性疲劳。     

沙漠道路曲线对非专职驾驶员心率的影响

为合理确定道路曲线,确保沙漠公路行车安全,利用多导生理记录仪研究实驾试验中沙漠道路曲线对非专职驾驶员心率的影响。通过频域分析和灰色关联分析,确定非专职驾驶员的高频段功率的标化值(HFnorm)和低频与高频功率的比值(LF/HF)与道路曲线的关系。结果表明:在景观比较单一的沙漠地区道路曲线线形设计中,适当增加不同半径的曲线,能在一定程度上缓解驾驶员的行车疲劳。沙漠道路曲线变化对于驾龄较长的年轻驾驶员迷走神经活动的影响比年长驾驶员的要小。为保证司机的自主神经活动处于合理的范围,沙漠道路平竖曲线长度的设计应根据驾驶员心电的LF/HF指标确定,纵坡度的设计应根据驾驶员心电的HFnorm和LH/HF指标综合确定。     

基于HRV的北方林区冰雪道路驾驶疲劳分析

为提高林区冰雪道路行车安全性,探讨林区冰雪道路驾驶员心理与生理疲劳状态,针对东北林区冰雪道路环境特殊性,通过实驾试验采集驾驶员的心电信号,基于心率变异性(HRV)指标的有效定量评价,结合指标间的灰色关联度分析,研究东北林区冰雪道路环境下驾驶员的心率(HR)、R-R间期标准偏差(SDNN)、低频与高频比值(LF/HF)及相关维数(D2)4个指标值间的变化规律及其随时间的变化规律。结果表明,驾驶员在林区冰雪道路驾驶过程中,HRV指标与HR关联次序为SDNN,D2,LF/HF,驾驶员在林区冰雪道路行车初期驾驶员紧张程度大;连续行车驾驶员精神负荷与体力负荷都会增大,出现疲劳累积效应。     

噪声对建筑施工人员作业疲劳影响实验研究

为了研究噪声对建筑施工人员作业疲劳的影响,设计噪声作业生理实验。先通过建筑施工作业疲劳分析确定疲劳测量生理指标,再通过施工作业现场噪声测量确定实验中作业环境噪声等级分类。根据实验中被试者心率指标变化率及作业疲劳自我评价量表对噪声作业疲劳程度影响进行分析,结果表明,施工人员作业疲劳受噪声影响;持续作业15min后噪声对施工人员作业疲劳程度影响显著;作业疲劳程度与作业环境中噪声值成正相关关系;施工人员作业后疲劳恢复程度不受作业环境噪声值大小影响。     

面向驾驶员心电数据的交叉口驾驶风险评估方法

为更精准地评估交叉口范围内的驾驶风险,首先,引入驾驶员心电(EGG)数据,提出基于余弦相似性距离的逼近理想解排序(TOPSIS)模型;其次,建立滚动时间窗法,并改进传统的短期频域指标计算方法,计算模型中低频(LF)变化率指标和低频与高频之比(LF/HF)等心率变异性(HRV)指标,模型时域指标有心脏搏动周期(R-R间期)变化率和其标准差(SDNN);然后,根据心电频域和时域指标与静息状态下对应指标的接近程度进行综合排序,按照越接近驾驶风险越小的原则,评估交叉口区域的驾驶风险;最后,选择30名驾驶员,在西安市23个交叉口开展实车试验,采集试验数据并验证模型方法。结果表明：驾驶员在相同交叉口环境下的驾驶风险评估结果相似性超90.1%,所提方法可应用稀疏样本评估交叉口的整体驾驶风险;高驾驶风险的交叉口评估方差较中、低驾驶风险路口分别高38.8%和67.9%,表明低风险交叉口区域驾驶风险的评估更精准。     

公路线形复杂程度对驾驶员HRV的影响研究

通过检测驾驶员心理、生理指标,观察公路线形复杂程度对驾驶员心率变异性(HRV)的影响,研究不合理公路线形对驾驶员行车安全的影响。对内蒙古境内6条公路的线形资料进行调查统计,利用赋值打分法确定各自的复杂程度;选取典型路段进行实驾试验,通过二列相关系数的计算及相应检验,得到心率变异分析的敏感指标;进而分析公路线形复杂程度对驾驶员心理、生理的影响。结果表明,HRV指标间期均值(MRR)、低高频比值(LF/HF)、样本熵(Samp En)对公路线形复杂程度响应敏感;即复杂程度过低会导致驾驶员由于驾驶任务简单出现反应迟缓、注意力难以集中等现象,影响行车安全;复杂程度过高会使驾驶员时刻保持精神高度集中状态,容易提前产生疲劳,从而威胁行车安全。     

草原公路短时程驾驶疲劳程度多指标划分研究

为确定行车过程中不同时段驾驶员的疲劳程度,考虑草原公路的特殊性,选取典型草原公路路段,对9位受试者腰部肌电(EMG)、脑电(EEG)及心电(ECG)信号进行连续3 h的实驾测试。用因子(降维)及相关性分析法得到疲劳敏感指标;通过回归方程得到疲劳公式;利用层次聚类法初步划分疲劳程度,并验证划分结果。试验结果表明:表征EMG信号频谱变化的中位频率(MF)、ECG信号的心率均值(MHR)、高频标准化值(HFnu)和EEG信号的(α+θ)/β值对草原公路驾驶疲劳响应敏感,且驾驶疲劳发展呈多元线性变化;草原公路短时程驾驶疲劳可分为3个阶段。     

井下支护作业人员颈部肌肉疲劳特性研究*

为探究井下支护作业人员颈部肌肉疲劳受伸张和屈曲角度变化的影响,采用表面肌电法（sEMG）实验模拟测量7种点位角度下,颈部夹肌、斜方肌以及胸锁乳突肌在不同作业频率时的疲劳情况,以积分肌电值(iEMG)及中值频率（MF）评价各肌肉疲劳程度。研究结果表明:低频实验中随点位角度的增大,颈部屈曲活动时,胸锁乳突肌疲劳变化明显,iEMG疲劳前后差值最大为1.55,MF下降率最大为0.60;颈部伸展活动时,夹肌疲劳变化明显,iEMG疲劳前后差值最大为1.59,MF下降率最大为0.59;斜方肌未表现出明显疲劳变化规律。高频实验相较低频实验疲劳发生速度加快,疲劳积累程度显著提升,颈部活动主要肌肉疲劳发生时间由16~19 min提前至13~17 min,各肌肉MF下降速度为低频实验的1~1.55倍。     

长时间单调模拟驾驶对疲劳的影响研究

通过模拟驾驶实验,综合评估长时间驾驶以及单调环境对驾驶员疲劳程度的影响是笔者研究的主要课题内容。借助于在模拟驾驶座舱上,4个健康样本分别参加高速公路(单调环境)和一般公路(非单调环境)的两组驾驶仿真实验,每组测试均持续两小时,一共进行10次实验。实验过程中,样本的操控数据(车速和方向盘转角)、反应时间、心电信号、主观疲劳状况等都同步记录并保存。实验结果表明长时间驾驶对操控能力、反应时间、心率、主观疲劳都有显著性影响(p<0.050),单调环境(高速公路)和非单调环境(一般公路)相比,车速方差区别显著,而尽管被试在高速公路的单调环境下驾驶后主观感觉更疲劳一些,但反应时间、心率等因素并没有显著性差异。     

Heart Rate Variability in Exposure to High Altitude Hypoxia of Short Duration

Abstract

The objective of the study is to attempt an evaluation of heart rate (HR) and heart rate variability (HRV) regulatory mechanisms in the presence of autonomous nervous system (ANS) components in transient exposure to high altitude hypoxia.

During 24 hrs including a stay in hypobaria, the participants had their HR continuously recorded using the Holter method. The following parameters were calculated at rest and during the stay in a thermobarochamber: spectral power in low frequency bands (LF) 0.04-0.15 Hz and high frequency bands (HF) 0.15-0.5 Hz, and the sympathetic-parasympathetic balance index LF/HF.

Under hypobaric conditions, a decrease in mean spectral power of R-R intervals was noted within both frequency ranges, compared with the study performed in normobaria. The observed differences were larger at daytime.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.     

火灾情境下认知生理反应与建筑物疏散标识优化研究

为研究火灾环境中人的认知生理变化规律,在心理实验室常规环境与受限空间火灾情境模拟中,分别运用E-Prime软件,对样本的心算、简单颜色、复杂颜色认知反应和心电生理反应进行了对照研究。结果表明:1)受限空间中的心算反应错误率高于心理实验室的测试结果,复杂颜色反应的错误率低于心理实验室的测试结果,两项目的反应时均值配对t检验均存在显著差异,简单颜色测试项目的错误率与反应时均值在对照试验中的变化均不显著;2)75%被试在情境模拟试验中的心率变化明显,配对t检验达到显著水平,15名被试心电图有小幅波动但波段振幅范围正常,5名被试出现轻微房颤与心律失常症状;3)基于人的认知生理反应规律,在建筑物现有疏散标识体系的基础上,提出了增设局部疏散标识的建议。     

The effects of personal music devices on pedestrian behaviour

It has been suggested that pedestrians listening to personal music devices (PMD) are more likely to be involved in accidents than those not listening to PMDs. Though it has been demonstrated that pedestrians on cell phones exhibit less cautionary behaviour when street crossing ( [Hatfield and Murphy, 2007], [Nasar et al., 2008] and [Stavrinos et al., 2009]), little research has been conducted with PMD users. In the present study, cautionary behaviour (e.g., looking before crossing a road) was observed and recorded for pedestrians with or without PMDs. Amongst males, pedestrians listening to PMDs displayed more looking behaviour than those not listening to PMDs. Females showed no differences between the two conditions. Thus, unlike cell phones, PMDs do not decrease the cautionary behaviour of pedestrians. This suggests that cell phones and PMDs are two different types of distractions, and this needs to be taken into account when developing methods to prevent pedestrian accidents in the future.     

基于层级分析法的航海人员驾驶疲劳因子分析

笔者从环境不同的基本要素切入,分析个人生理、心理和技术对航海人员驾驶疲劳因子之影响,提出以全面性的环境差别建构航海人员驾驶疲劳因子指标体系,应用层级分析法对该体系进行评价,该评价方法简单、实用,且有清晰的架构,能完整呈现专家的意见。“工作疲劳靠休息;心理压力靠抒解”,建议以正面激励的安全驾驶管理,建立航海驾驶人员疏解疲劳之途径,且鼓励船员相互砥砺并适度给予奖励,以消除船员驾驶疲劳因子产生的根源。     

长隧道韵律型视觉环境设计研究

采用心理物理试验分析公路隧道内部视觉环境对驾驶员行车安全的影响,将E-prime 2.0软件与仿真驾驶模拟器相结合,对驾驶员在隧道内长时间行车中的速度判断准确率及反应时间两个指标进行分析,提出了利用标志标线构建公路隧道内韵律型标线系统的改善措施,以改善隧道内视觉环境,并利用数理统计方法及Logistics拟合分析对设计方案进行评价。结果表明:1)公路隧道内韵律型标线系统能提升隧道内驾驶员的速度判断准确率3.33%~11.66%;2)普通公路隧道场景中,被试者反应时间与隧道内行车时间存在显著关系,公路隧道内韵律型标线系统的场景中,反应时间与隧道内的行车时间没有显著关系,能有效缓解视觉疲劳现象;3)被试者反应时间的增加同时受隧道内视觉环境与行车时间的影响。公路隧道内韵律型标线系统能有效提高驾驶员的反应时间,适用于行驶速度为80 km/h、大于1 333 m的隧道。     

合理构建视频显示终端(VDT)作业疲劳工间休息制度

为了减少VDT(视频显示终端)作业对操作者健康的负面影响,合理地安排工作和休息时间以及设计良好的工作环境有助于减轻、缓解或消除VDT作业的疲劳。通过多项VDT数据输入作业实验,依据作业者作业能力变动的规律性,对不同类型的VDT作业者可能需要设置不同的工间休息制度。经过对实验数据的分析,数据输入的工作主要是信息接受和信息输出,属于事务性脑力劳动。只要合理地安排工作和休息时间以及设计良好的工作环境,就可以减少VDT对眼睛、骨骼及肌肉的疾患发生。     

基于EEG的有限空间检修工脑疲劳特性研究

为了探究有限空间内检修工在高温高湿状态下的脑疲劳特征，采用对照与模拟有限空间作业环境相结合的方法，对作业人员脑电波变化进行监测，利用傅里叶变换对原波信号进行时频转换，提取相对频率、重心频率等特征参数。研究结果表明:在高温高湿的有限空间环境中，人体脑波功率变化幅度明显变大；在进入疲劳状态时有限空间环境的脑波功率低于对照组；高频的β波在高温高湿环境中减少的比例较大，平均下降7.1%；脑波重心频率由8 Hz降为6 Hz，平均下降11.3%，由α波变为θ波，大脑活动进入疲劳状态。说明有限空间的高温高湿环境对人体脑疲劳具有明显影响，可用于脑电智能穿戴监测设备监测工人脑疲劳状况。     

Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue

Introduction: Fatigue is one of the most crucial factors that contribute to a decrease of the operating performance of aircraft pilots and car drivers and, as such, plays a dangerous role in transport safety. To reduce fatigue-related tragedies and to increase the quality of a healthy life, many studies have focused on exploring effective methods and psychophysiological indicators for detecting and monitoring fatigue. However, those fatigue indicators rose many discrepancies among simulator and field studies, due to the vague conceptualism of fatigue, per se, which hinders the development of fatigue monitoring devices. Method: This paper aims to give psychological insight of the existing non-invasive measures for driver and pilot fatigue by differentiating sleepiness and mental fatigue. Such a study helps to improve research results for a wide range of researchers whose interests lie in the development of in-vehicle fatigue detection devices. First, the nature of fatigue for drivers/pilots is elucidated regarding fatigue types and fatigue responses, which reshapes our understanding of the fatigue issue in the transport industry. Secondly, the widely used objective neurophysiological methods, including electroencephalography (EEG), electrooculography (EOG), and electrocardiography (ECG), physical movement-based methods, vehicle-based methods, fitness-for-duty test as well as subjective methods (self-rating scales) are introduced. On the one hand, considering the difference between mental fatigue and sleepiness effects, the links between the objective and subjective indicators and fatigue are thoroughly investigated and reviewed. On the other hand, to better determine fatigue occurrence, a new combination of measures is recommended, as a single measure is not sufficient to yield a convincing benchmark of fatigue. Finally, since video-based techniques of measuring eye metrics offer a promising and practical method for monitoring operator fatigue, the relationship between fatigue and these eye metrics, that include blink-based, pupil-based, and saccade-based features, are also discussed. To realize a pragmatic fatigue detector for operators in the future, this paper concludes with a discussion on the future directions in terms of methodology of conducting operator fatigue research and fatigue analysis by using eye-related parameters.     

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.

Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.

Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between ?0.0011 and 0.056 for a car driver and between ?0.00086 and 0.067 for a truck driver.

Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.